A comprehensive profile of the sociodemographic, psychosocial and health characteristics of Ontario home care clients with dementia

Abstract Introduction: This study provides a comprehensive summary of the sociodemographic, psychosocial and health characteristics of a large population-based cohort of Ontario home care clients (aged 50 years and over) with dementia and examines the variation in these characteristics in those with co-existing neurological conditions

Nursing Home Prices and Quality of Care - Evidence from Administrative Data

There is widespread concern about the quality of care in nursing homes. Based on administrative data of a large health insurance fund, we investigate whether nursing home prices affect relevant quality of care indicators at the resident level. Our results indicate a significantly negative price effect on inappropriate and psychotropic medication. In contrast, we find no evidence for fewer painful physical sufferings for residents of nursing homes with higher prices.Die in den letzten Jahren ins Blickfeld der Öffentlichkeit getretenen Probleme in Pflegeheimen führten zu dem vielfach geäußerten Verdacht der medikamentösen Ruhigstellung betreuungsbedürftiger Menschen in der stationären Pflege. Mit der vorliegenden Untersuchung soll ein Beitrag zum Abbau der derzeit noch bestehenden Wissensdefizite über den Zusammenhang zwischen dem Preis und der Qualität der stationären Pflegeleistungen geleistet werden. Auf Basis von Routinedaten der Techniker Krankenkasse werden hierzu neben der Inzidenz an Verletzungen sowie weiteren gesundheitlichen Beeinträchtigungen aufgrund äußerer Umstände die verordnete Dosis potenziell inadäquater Medikamente für ältere Menschen sowie weiterer Arzneistoffklassen (u.a. Psycholeptika) als Indikatoren der Ergebnisqualität herangezogen. Die Ergebnisse der Untersuchung zeigen einen negativen Effekt der Heimpreise auf Verschreibungen potenziell inadäquater und psychotropischer Medikamente

Meta-Learning Probabilistic Inference For Prediction

This paper introduces a new framework for data efficient and versatile learning. Specifically: 1) We develop ML-PIP, a general framework for Meta-Learning approximate Probabilistic Inference for Prediction. ML-PIP extends existing probabilistic interpretations of meta-learning to cover a broad class of methods. 2) We introduce VERSA, an instance of the framework employing a flexible and versatile amortization network that takes few-shot learning datasets as inputs, with arbitrary numbers of shots, and outputs a distribution over task-specific parameters in a single forward pass. VERSA substitutes optimization at test time with forward passes through inference networks, amortizing the cost of inference and relieving the need for second derivatives during training. 3) We evaluate VERSA on benchmark datasets where the method sets new state-of-the-art results, handles arbitrary numbers of shots, and for classification, arbitrary numbers of classes at train and test time. The power of the approach is then demonstrated through a challenging few-shot ShapeNet view reconstruction task